
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    :			str    =	{
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Any    =	["""ConvBertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Optional[int]    =	[
							    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """ConvBertForMaskedLM""",
							    """ConvBertForMultipleChoice""",
							    """ConvBertForQuestionAnswering""",
							    """ConvBertForSequenceClassification""",
							    """ConvBertForTokenClassification""",
							    """ConvBertLayer""",
							    """ConvBertModel""",
							    """ConvBertPreTrainedModel""",
							    """load_tf_weights_in_convbert""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			str    =	[
							    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFConvBertForMaskedLM""",
							    """TFConvBertForMultipleChoice""",
							    """TFConvBertForQuestionAnswering""",
							    """TFConvBertForSequenceClassification""",
							    """TFConvBertForTokenClassification""",
							    """TFConvBertLayer""",
							    """TFConvBertModel""",
							    """TFConvBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
							from .tokenization_convbert import ConvBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_convbert_fast import ConvBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_convbert import (
														    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConvBertForMaskedLM,
														    ConvBertForMultipleChoice,
														    ConvBertForQuestionAnswering,
														    ConvBertForSequenceClassification,
														    ConvBertForTokenClassification,
														    ConvBertLayer,
														    ConvBertModel,
														    ConvBertPreTrainedModel,
														    load_tf_weights_in_convbert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_convbert import (
														    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFConvBertForMaskedLM,
														    TFConvBertForMultipleChoice,
														    TFConvBertForQuestionAnswering,
														    TFConvBertForSequenceClassification,
														    TFConvBertForTokenClassification,
														    TFConvBertLayer,
														    TFConvBertModel,
														    TFConvBertPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_    :			Optional[int]    =	{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Union[str, Any]    =	[
							    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GraphormerForGraphClassification""",
							    """GraphormerModel""",
							    """GraphormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






